This repository contains my solutions to the lab sessions for the course CS F437: Generative AI. Each lab focuses on different aspects and applications of generative models. I have included my solutions, the original lab instructions, and any necessary data or results files.
- Lab 1: Introduction to Python and PyTorch
- Lab 2: Neural Networks (with Python)
- Lab 3: Text generation with Logistic regression & RNNs.
- Lab 4: Image generation
- Lab 5: Pixel RNNs
- Lab 6: Pixel CNNs and Autoencoders
- Lab 7: Variational AutoEncoders (VAEs)
- Lab 8: TRANSFORMERS
- Lab 9: Generative Adversarial Networks (GANs)
- Lab 10: Image Generation using Diffusion Models
- Lab 11: Normalizing Flows
To run the solutions on your local machine, follow these steps:
- Clone the repository:
git clone https://github.com/Ojasva-Goyal/CS-F437--Generative-AI---Labs.git
cd CS-F437--Generative-AI---Labs - Install necessary dependencies. It's recommended to use a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
- Navigate to the respective lab directory and open the Jupyter notebook:
cd Lab_<Lab_No>
jupyter notebook <solution_file_name>.ipynb
Note: Ensure you have Jupyter installed. If not, you can install it via pip:
pip install jupyterContributions are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request.
This project is licensed under the GNU GENERAL PUBLIC LICENSE - see the LICENSE file for details.
Created by Ojasva Goyal - feel free to contact me at [email protected] for any questions or feedback.